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Background <p>Myasthenia Gravis (MG) is divided into ocular (OMG) and generalized (GMG) subtypes. While clinical diagnosis is well-established, understanding the underlying biochemical mechanisms and metabolic shifts during disease progression remains challenging; untargeted metabolomics offers a novel perspective to explore these systemic alterations.</p> Objective <p>To characterize the serum metabolic landscape of MG patients and identify potential metabolic signatures associated with disease subtypes (OMG and GMG) via untargeted metabolomics.</p> Methods <p>91 participants (41 GMG, 22 OMG, 28 healthy controls [HC]) were enrolled. Fasting serum samples were analyzed by LC-MS/MS. Multivariate analyses (PCA, PLS-DA/OPLS-DA), differential metabolite screening (VIP &gt; 1.0, <i>p</i> &lt; 0.05), and KEGG pathway enrichment were performed.</p> Results <p>HC and MG groups showed distinct metabolic profiles. MG had 515 (175 up, 340 down) and 368 (146 up, 222 down) differential metabolites in positive/negative ion modes, respectively. Key perturbed pathways included glycerophospholipid, sphingolipid metabolism, and unsaturated fatty acid biosynthesis. Ten representative metabolites (e.g., ubiquinone, cortisol) differed significantly among groups; clustering analysis revealed distinct metabolite abundance trajectories across HC, OMG, and GMG.</p> Conclusion <p>MG is associated with notable systemic metabolic dysregulation, particularly in lipid-related pathways. Rather than serving as immediate diagnostic tools, these integrative metabolic signatures provide a crucial biochemical framework for understanding disease pathogenesis and offer valuable clues for future hypothesis-driven research and prospective validation.</p>

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Untargeted serum metabolomic profiling in patients with generalized and ocular myasthenia gravis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Bin Wei,
  • Haoyu Yuan,
  • Jiawei He,
  • Yuxiang Hu,
  • Jing Li,
  • Xiaorong Wu

摘要

Background

Myasthenia Gravis (MG) is divided into ocular (OMG) and generalized (GMG) subtypes. While clinical diagnosis is well-established, understanding the underlying biochemical mechanisms and metabolic shifts during disease progression remains challenging; untargeted metabolomics offers a novel perspective to explore these systemic alterations.

Objective

To characterize the serum metabolic landscape of MG patients and identify potential metabolic signatures associated with disease subtypes (OMG and GMG) via untargeted metabolomics.

Methods

91 participants (41 GMG, 22 OMG, 28 healthy controls [HC]) were enrolled. Fasting serum samples were analyzed by LC-MS/MS. Multivariate analyses (PCA, PLS-DA/OPLS-DA), differential metabolite screening (VIP > 1.0, p < 0.05), and KEGG pathway enrichment were performed.

Results

HC and MG groups showed distinct metabolic profiles. MG had 515 (175 up, 340 down) and 368 (146 up, 222 down) differential metabolites in positive/negative ion modes, respectively. Key perturbed pathways included glycerophospholipid, sphingolipid metabolism, and unsaturated fatty acid biosynthesis. Ten representative metabolites (e.g., ubiquinone, cortisol) differed significantly among groups; clustering analysis revealed distinct metabolite abundance trajectories across HC, OMG, and GMG.

Conclusion

MG is associated with notable systemic metabolic dysregulation, particularly in lipid-related pathways. Rather than serving as immediate diagnostic tools, these integrative metabolic signatures provide a crucial biochemical framework for understanding disease pathogenesis and offer valuable clues for future hypothesis-driven research and prospective validation.